Abstract: Context: The accurate estimation of landscape resistance to movement is important for ecological understanding and conservation applications. Rigorous estimation of resistance requires validation and optimization. One approach uses genetic data for the optimization or validation of resistance models. Objectives We used a genetic dataset of snow leopards from China to evaluate how landscape genetics resistance models varied across genetic distances and spatial scales of analysis. We evaluated whether landscape genetics models were superior to models of resistance derived from habitat suitability or isolation-by-distance.
Methods: We regressed genetically optimized, habitat-based, and isolation-by-distance hypotheses against genetic distances using mixed effect models. We explored all subset combinations of genetically optimized variables to find the most supported resistance scenario for each genetic distance.
Results: Genetically optimized models always out-performed habitat-based and isolation-by-distance hypotheses. The choice of genetic distances influenced the apparent influence of variables, their spatial scales and their functional response shapes, producing divergent resistance scenarios. Gene flow in snow leopards was largely facilitated by areas of intermediate ruggedness at intermediate elevations corresponding to small-to-large valleys within and between the mountain ranges.
Conclusions: This study highlights that landscape genetics models provide superior estimation of functional dispersal than habitat surrogates and suggests that optimization of genetic distance should be included as an optimization routine in landscape genetics, along with variables, scales, effect size and functional response shape. Furthermore, our study provides new insights on the ecological conditions that promote gene flow in snow leopards, which expands ecological knowledge, and we hope will improve conservation planning.

Abstract: The author reports the status of the captive population of snow leopards over the last three decades. Genetic and demographic information is also provided. The captive population as of 1992 was 541 leopards. klf. I

Abstract: An extraction and assay procedure to measure fecal estradiol-17-beta and progestin concentrations in several cat species was developed and validated for use for noninvasive monitoring of ovarian function. Fecal samples were collected over a range of 3-20 months from female tigers (three), lions (three), snow leopards (three), cheetahs (two), caracals (two), and domestic cats (five). Samples were extracted with 90% methanol, lipids removed with petroleum ether, and the estradiol and progestins in the methanol measured by radioimmunoassay (RIA). High Performance Liquid Chromatography (HPLC) fractionation and subsequent RIA of the fractions indicated that the estradiol-17-beta antiserum cross-reacted primarily with estradiol-17-beta in the feces of lions and tigers and was assumed to be specific for estradiol-17-beta in the feces of other species as well. However, there were several immunoreactive compounds, presumably progesterone metabolites, excreted in the feces which varied both quantitatively and qualitatively among species. The behavior of tigers, lions, cheetahs, and caracals was visually monitored during the collection period and frequency of sexual behaviors was positively correlated with increases in fecal estradiol in all species observed. The mean fecal estradiol-17-beta peaks were as follows: tigers, 128.0 +- 13.1; lions, 186.0 +- 14.8; snow leopards, 136.7 +- 15.9; cheetahs, 140.9 +- 9.0; caracals, 24.5 +- 4.0; and domestic cats 158.9 +- 19.3 ng/gm. Fecal progestin concentrations rose significantly (P lt 0,001) only after breeding or during pregnancy and were as follows: tigers, 5.6 +- 0.6; lions, 1.9 +- 0.1; cheetahs, 8.4 +- 1.1; and caracals, 2.4 +- 0.4 mu-g/gm. Fecal progestins were elevated for one-half to two-thirds of the gestation length during presumed pseudopregnancy but remained elevated throughout successful pregnancies. These results suggest that ovarian function can be monitored noninvasively in the family Felidae by the measurement of fecal estradiol-17-beta and progestin concentrations.

Abstract: Understanding landscape connectivity and population genetic parameters is imperative for threatened species management. However, such information is lacking for the snow leopard (Panthera uncia). This study sought to explore hierarchical snow leopard gene flow patterns and drivers of genetic structure in Mongolia and China. A total of 97 individuals from across Mongolia and from the north-eastern edge of the Qinghai-Tibetan Plateau in Gansu Province to the middle of Qinghai Province in China were genotyped across 24 microsatellite loci. Distance-based frameworks were used to determine a landscape scenario best explaining observed genetic structure. Spatial and non-spatial methods were used to investigate fine-scale autocorrelation and similarity patterns as well as genetic structure and admixture. A genetic macro-division between populations in China and Mongolia was observed, suggesting that the Gobi Desert is a substantial barrier to gene flow. However, admixture and support for a resistance-based mode of isolation suggests connective routes that could facilitate movement. Populations in Mongolia had greater connectivity, indicative of more continuous habitat. Drivers of genetic structure in China were difficult to discern, and fine-scale sampling is needed. This study elucidates snow leopard landscape connectivity and helps to prioritize conservation areas. Although contact zones may have existed and occasional crossings can occur, establishing corridors to connect these areas should not be a priority. Focus should be placed on maintaining the relatively high connectivity for snow leopard populations within Mongolia and increasing research efforts in China.